doi: 10.7873/DATE.2015.1116
Opportunities for Energy Efficient Computing: A Study of Inexact General Purpose Processors for HighPerformance and BigData Applications
Peter Düben^{1,a}, Jeremy Schlachter^{2,c}, Parishkrati^{3,e}, Sreelatha Yenugula^{3,f}, John Augustine^{3,g}, Christian Enz^{2,d}, K. Palem^{4} and T. N. Palmer^{1,b}
^{1}AOPP, University of Oxford Oxford, United Kingdom.
^{a}dueben@atm.ox.ac.uk
^{b}t.n.palmer@atm.ox.ac.uk
^{2}EPFL Lausanne, Switzerland.
^{c}jeremy.schlachter@epfl.ch
^{d}christian.enz@epfl.ch
^{3}Indian Institute of Technology Madras India.
^{e}parishkratihhh@gmail.com
^{f}sreelathayenugula@gmail.com
^{g}augustine@cse.iitm.ac.in
^{4}Electrical Engineering and Computer Science Rice University, Houston, USA.
kvp1@rice.edu
ABSTRACT
In this paper, we demonstrate that disproportionate
gains are possible through a simple devise for injecting inexactness
or approximation into the hardware architecture of a computing
system with a general purpose template including a complete
memory hierarchy. The focus of the study is on energy savings
possible through this approach in the context of large and
challenging applications. We choose two such from different ends of
the computing spectrum–the IGCM model for weather and climate
modeling which embodies significant features of a highperformance
computing workload, and the ubiquitous PageRank algorithm used in
Internet search. In both cases, we are able to show in the affirmative
that an inexact system outperforms its exact counterpart in terms of
its efficiency quantified through the relative metric of operations per
virtual Joule (OPVJ)–a relative metric that is not tied to particular
hardware technology. As one example, the IGCM application can be
used to achieve savings through inexactness of (almost) a factor of 3
in energy without compromising the quality of the forecast,
quantified through the forecast error metric, in a noticeable manner.
As another example finding, we show that in the case of PageRank,
an inexact system is able to outperform its exact counterpart by close
to a factor of 1.5 using the OPVJ metric.
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